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The importance of diversifying the national STEM workforce is well-established in the literature (Marrongelle, 2018). This need extends to graduate education in the STEM fields, leading N.C. A&T to invest considerably in graduate education and wraparound support initiatives that help graduate students build science identity and competencies for careers both within and beyond academia. The NSF-funded Bridges to the Doctorate project will integrate culturally reflective mentoring and professional development specifically designed for Black, Latinx, and Native American Ph.D. students. This holistic, graduate student development model includes academic and professional skill-building for STEM careers alongside targeted support for pursuing fellowship opportunities. This paper discusses the planned mentoring approach for the aforementioned program and previous approaches to mentoring graduate students used at N.C. A&T. The BD Fellows program will support formal and informal mentoring relationships, as mentoring contributes towards retention in STEM graduate programs (Ragins, 2007). BD Fellows will participate in monthly one-hour seminars on how to identify, establish, and maintain informal mentoring relationships (Schwartz et al., 2018; Parnes et al., 2020), while STEM faculty will attend seminars on leveraging their social networks as vital sources of mentorship for the BD Fellows. Using a multi-pronged collaborative approach, this model integrates the evidence-based domains of self-efficacy (Laurencelle & Scanlan, 2018; Lent et al., 1994; Lent et al., 2008), science/research identity (Lent et al., 2015; Zimmerman, 2000), and social cognitive career theory (Lent et al., 2005; Lent and Brown, 2006) to recruit, enroll, and graduate LSAMP Fellows with STEM doctoral degrees. Guided by the theories, the following questions will be addressed: (1) To what extent is culturally reflective mentoring identified as a critical driver of B2D Fellows’ success? (2) To what extent are the program’s training components fostering increases in B2D Fellow’s self-efficacy, competency, and science identity? (3) What is the strength of the correlation between participation in the program training components, mentoring activities, and persistence in graduate school? (4) To what extent does the perceived importance of self-efficacy, competency, and science identity differ by race/ethnicity and gender? These data will be analyzed using both formative and summative assessments of program outcomes. Quantitative data will include pre-, post-, and exit surveys. Qualitative data will assess the impact of mentoring and program support. This study will be guided by established protocols that have been approved by the N.C. A&T IRB. It is anticipated that our BD Fellows program will significantly impact the retention and graduation rates of underrepresented minority STEM graduate students in our doctoral programs, thus producing a diverse workforce of STEM professionals. Materials from the program recruiting cycle, mentoring workshops, and the structured fellowship application process will be disseminated freely to other LSAMP and minority-serving institutions across the country. Strategies and outcomes of this project will be published in peer-reviewed journals and shared in conference proceedings. 

Abstract The Pandora Software Development Kit and algorithm libraries provide pattern-recognition logic essential to the reconstruction of particle interactions in liquid argon time projection chamber detectors. Pandora is the primary event reconstruction software used at ProtoDUNE-SP, a prototype for the Deep Underground Neutrino Experiment far detector. ProtoDUNE-SP, located at CERN, is exposed to a charged-particle test beam. This paper gives an overview of the Pandora reconstruction algorithms and how they have been tailored for use at ProtoDUNE-SP. In complex events with numerous cosmic-ray and beam background particles, the simulated reconstruction and identification efficiency for triggered test-beam particles is above 80% for the majority of particle type and beam momentum combinations. Specifically, simulated 1 GeV/ c charged pions and protons are correctly reconstructed and identified with efficiencies of 86.1 $$\pm 0.6$$ ± 0.6 % and 84.1 $$\pm 0.6$$ ± 0.6 %, respectively. The efficiencies measured for test-beam data are shown to be within 5% of those predicted by the simulation.